Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Factorial Design02:01

Factorial Design

13.3K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.3K
Response Surface Methodology01:16

Response Surface Methodology

312
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
312
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

325
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
325
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

689
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
689
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

806
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
806
One-Way ANOVA01:18

One-Way ANOVA

8.6K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
8.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A lightweight YOLOv8n-based method for rail surface defect detection in complex railway environments.

Scientific reports·2026
Same author

Asymptotic standard errors for reliability coefficients in item response theory.

The British journal of mathematical and statistical psychology·2026
Same author

Enhancing Two-Stage Estimation in Differential Equation Models: A Bias-Correction Method via Stochastic Approximation.

Psychometrika·2026
Same author

A New Fit Assessment Framework for Common Factor Models Using Generalized Residuals.

Psychometrika·2025
Same author

Understanding measurement precision from a regression perspective.

Psychological methods·2025
Same author

A hybrid security protocol based on honey encryption and hyperchaotic systems for improving security in internet of things.

Scientific reports·2025

Related Experiment Video

Updated: Oct 5, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.3K

Semiparametric Factor Analysis for Item-Level Response Time Data.

Yang Liu1, Weimeng Wang2

  • 1Department of Human Development and Quantitative Methodology, University of Maryland, College Park, USA. yliu87@umd.edu.

Psychometrika
|January 31, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible semiparametric factor model for analyzing response time (RT) data. The new model accurately captures complex relationships between observed RT and latent processing speed without strict assumptions.

Keywords:
Conditional density estimationCubic splineExpectation–maximization algorithmFactor analysisFunctional ANOVAPenalized maximum likelihood

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K
A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
08:01

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

9.1K

Related Experiment Videos

Last Updated: Oct 5, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.3K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K
A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
08:01

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

9.1K

Area of Science:

  • Psychometrics
  • Cognitive Psychology
  • Statistical Modeling

Background:

  • Item-level response time (RT) data from computer-based assessments offer insights into cognitive processes and test-taking behaviors.
  • Traditional factor analysis models for RT data impose strict parametric assumptions, limiting their flexibility in capturing complex associations with latent speed.
  • Existing models may sacrifice interpretability for flexibility or vice versa, necessitating more adaptable analytical approaches.

Purpose of the Study:

  • To propose a novel semiparametric factor model for analyzing item-level response time data.
  • To offer a more flexible alternative to conventional parametric models by minimizing restrictive assumptions.
  • To accurately model the complex associations between observed response times and latent processing speed.

Main Methods:

  • Development of a semiparametric factor model utilizing a functional analysis of variance representation.
  • Approximation of conditional density functions using cubic splines for main effects and interactions.
  • Estimation via an Expectation-Maximization algorithm with penalized maximum likelihood estimation; penalty weights determined by cross-validation.

Main Results:

  • Simulation studies demonstrated the semiparametric model's superior ability to recover data-generating mechanisms compared to misspecified parametric models.
  • The proposed model shows advantages in handling complex associations inherent in response time data.
  • A real-world data example confirmed the practical utility and benefits of the semiparametric approach.

Conclusions:

  • The proposed semiparametric factor model provides a flexible and robust framework for analyzing item-level response time data.
  • This method overcomes limitations of traditional parametric models by accommodating complex relationships between observed RT and latent speed.
  • The approach enhances the accurate inference of cognitive processes and individual differences from response time measurements.